[docs] Update examples in the PyTorch+IREE guide (iree-org#18620)

Progresses towards iree-org#18564.

Fixes an example involving `shark_turbine.aot` that produced a segfault
due to a call to `VmModule.wrap_buffer()` when run.

Also removes examples related to `aot.jittable` that were present in the
doc, and updates the recommended PyTorch version.

docs/website/docs/guides/ml-frameworks/pytorch.md

@@ -65,11 +65,12 @@ graph LR
 
 ## :octicons-download-16: Prerequisites
 
-Install a recent version of PyTorch (`2.3.0+`, prerelease as of April 2024):
+Install a recent version of PyTorch
+(`2.4.1`, latest stable release as of September 2024):
 
 ``` shell
 python -m pip install \
-  --pre --index-url https://download.pytorch.org/whl/test/cpu torch==2.3.0
+  --index-url https://download.pytorch.org/whl/test/cpu torch==2.4.1
 ```
 
 Install iree-turbine:
@@ -218,7 +219,7 @@ binary = export_output.compile(save_to=None)
 # Use the IREE runtime API to test the compiled program.
 config = ireert.Config("local-task")
 vm_module = ireert.load_vm_module(
-    ireert.VmModule.wrap_buffer(config.vm_instance, binary.map_memory()),
+    ireert.VmModule.copy_buffer(config.vm_instance, binary.map_memory()),
     config,
 )
 input = np.array([1.0, 2.0, 3.0, 4.0], dtype=np.float32)
@@ -297,59 +298,6 @@ exported function (typically called "forward"), while more complex programs can
 have multiple computation functions, initialization functions, "backward"
 methods for training, state management functions, debugging functions, etc.
 
-* Each instance method on a `aot.CompiledModule`-derived class is exported.
-  These instance methods can include calls to other `aot` components, such as
-  `aot.jittable` compute functions:
-
-    ```python
-    class GetOnesModule(aot.CompiledModule):
-      @aot.jittable
-      def compute_ones():
-        return torch.ones(3)
-
-      def get_ones(self):
-        return self.compute_ones()
-    ```
-
-* Instance methods can use `aot.AbstractTensor` to specify data types:
-
-    ```python hl_lines="8-9"
-    class IntSumModule(aot.CompiledModule):
-      @aot.jittable
-      def compute_sum(a, b):
-        return a + b
-
-      def sum_int32(
-        self,
-        a=aot.AbstractTensor(2, dtype=torch.int32),
-        b=aot.AbstractTensor(2, dtype=torch.int32),
-      ):
-        return self.compute_sum(a, b)
-    ```
-
-* Shapes can be made dynamic using `aot.AbstractTensor` and `aot.jittable`
-  constraints:
-
-    ```python hl_lines="8-9 14-16"
-    class DynamicSumModule(aot.CompiledModule):
-      @aot.jittable
-      def compute_sum(a, b):
-        return a + b
-
-      def sum_dynamic(
-        self,
-        a=aot.AbstractTensor(None),
-        b=aot.AbstractTensor(None),
-      ):
-        return self.compute_sum(
-            a,
-            b,
-            constraints=[
-                a.dynamic_dim(0) == b.dynamic_dim(0),
-            ],
-        )
-    ```
-
 #### :material-variable: Global variables
 
 _Global variables_ are used to represent persistent state within a program
@@ -374,38 +322,6 @@ their values independently at runtime.
         self.value = new_value
     ```
 
-* All named parameters on a `nn.Module` can be exported using
-  `export_parameters()`:
-
-    ```python hl_lines="12 18-26"
-    class SimpleParams(torch.nn.Module):
-      def __init__(self):
-        super().__init__()
-        self.classifier = torch.nn.Linear(20, 30)
-
-      def forward(self, x):
-        return self.classifier(x)
-
-    m = SimpleParams()
-
-    class SimpleParamsModule(aot.CompiledModule):
-      params = aot.export_parameters(m)
-      compute = aot.jittable(m.forward)
-
-      def run(self, x=aot.AbstractTensor(128, 20)):
-        return self.compute(x)
-
-      # torch.nn.Linear has 'weight' and 'bias' variables:
-      #   https://pytorch.org/docs/stable/generated/torch.nn.Linear.html
-      # Add getters for both exported parameters.
-
-      def get_weight(self):
-        return self.params["classifier.weight"]
-
-      def get_bias(self):
-        return self.params["classifier.bias"]
-    ```
-
 #### :octicons-code-16: Samples
 
 | Code samples |  |